Wearable robots assist individuals with sensorimotor impairment in daily life, or support industrial workers in physically demanding tasks. In such scenarios, low mass and compact design are crucial factors for device acceptance. Remote actuation systems (RAS) have emerged as a popular approach in wearable robots to reduce perceived weight and increase usability. Different RAS have been presented in the literature to accommodate for a wide range of applications and related design requirements. The push toward use of wearable robotics in out-of-the-lab applications in clinics, home environments, or industry created a shift in requirements for RAS. In this context, high durability, ergonomics, and simple maintenance gain in importance. However, these are only rarely considered and evaluated in research publications, despite being drivers for device abandonment by end-users. In this paper, we summarize existing approaches of RAS for wearable assistive technology in a literature review and compare advantages and disadvantages, focusing on specific evaluation criteria for out-of-the-lab applications to provide guidelines for the selection of RAS. Based on the gained insights, we present the development, optimization, and evaluation of a cable-based RAS for out-of-the-lab applications in a wearable assistive soft hand exoskeleton. The presented RAS features full wearability, high durability, high efficiency, and appealing design while fulfilling ergonomic criteria such as low mass and high wearing comfort. This work aims to support the transfer of RAS for wearable robotics from controlled lab environments to out-of-the-lab applications.Soft robotics has widely been known for its compliant characteristics when dealing with contraction or manipulation. These soft behavior patterns provide safe and adaptive interactions, greatly relieving the complexity of active control policies. However, another promising aspect of soft robotics, which is to achieve useful information from compliant behavior, is not widely studied. This characteristic could help to reduce the dependence of sensors, gain a better knowledge of the environment, and enrich high-level control strategies. In this paper, we have developed a state-change model of a soft robotic arm, and we demonstrate how compliant behavior could be used to estimate external load based on this model. Moreover, we propose an improved version of the estimation procedure, further reducing the estimation error by compensating the influcence of pressure deadzone. Experiments of both methods are compared, displaying the potential effectiveness of applying these methods.We present control policies for use with a modified autonomous underwater glider that are intended to enable remote launch/recovery and long-range unattended survey of the Arctic's marginal ice zone (MIZ). This region of the Arctic is poorly characterized but critical to the dynamics of ice advance and retreat. Due to the high cost of operating support vessels in the Arctic, the proposed glider architecture minimizes external infrastructure requirements for navigation and mission updates to brief and infrequent satellite updates on the order of once per day. This is possible through intelligent power management in combination with hybrid propulsion, adaptive velocity control, and dynamic depth band selection based on real-time environmental state estimation. We examine the energy savings, range improvements, decreased communication requirements, and temporal consistency that can be attained with the proposed glider architecture and control policies based on preliminary field data, and we discuss a future MIZ survey mission concept in the Arctic. Although the sensing and control policies presented here focus on under ice missions with an unattended underwater glider, they are hardware independent and are transferable to other robotic vehicle classes, including in aerial and space domains.Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights into the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path following and collision avoidance, decision making becomes nontrivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques to develop autonomous agents capable of achieving this hybrid objective without having a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path following and avoiding collisions towards achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.Charcot-Marie-Tooth disease (CMT) is one of the most common inherited neurological disorders. Despite the common involvement of ganglioside-induced differentiation-associated protein 1 (GDAP1) in CMT, the protein structure and function, as well as the pathogenic mechanisms, remain unclear. We determined the crystal structure of the complete human GDAP1 core domain, which shows a novel mode of dimerization within the glutathione S-transferase (GST) family. The long GDAP1-specific insertion forms an extended helix and a flexible loop. GDAP1 is catalytically inactive toward classical GST substrates. https://www.selleckchem.com/products/PI-103.html Through metabolite screening, we identified a ligand for GDAP1, the fatty acid hexadecanedioic acid, which is relevant for mitochondrial membrane permeability and Ca2+ homeostasis. The fatty acid binds to a pocket next to a CMT-linked residue cluster, increases protein stability, and induces changes in protein conformation and oligomerization. The closest homologue of GDAP1, GDAP1L1, is monomeric in its full-length form. Our results highlight the uniqueness of GDAP1 within the GST family and point toward allosteric mechanisms in regulating GDAP1 oligomeric state and function.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, as coronavirus disease 2019 (COVID-19) pandemic, has killed more than a million people worldwide, and researchers are constantly working to develop therapeutics in the treatment and prevention of this new viral infection. To infect and induced pathogenesis as observed in other viral infections, we postulated that SARS-CoV-2 may also require an escalation in the anabolic metabolism, such as glucose and glutamine, to support its energy and biosynthetic requirements during the infection cycle. Recently, the requirement of altered glucose metabolism in SARS-CoV-2 pathogenesis was demonstrated, but the role of dysregulated glutamine metabolism is not yet mentioned for its infection. In this perspective, we have attempted to provide a summary of possible biochemical events on putative metabolic reprograming of glutamine in host cells upon SARS-CoV-2 infection by comparison to other viral infections/cancer metabolism and available clinical data or research on SARS-CoV-2 pathogenesis.